LRR Model Research Articles

Bankline Migration Analysis of Brahmaputra River in Morigaon District, Assam using Automated Method

Objectives: The study examines spatio-temporal changes, forecasts bankline migration and seeks to determine its underlying causes and consequences. Methods: Bankline change detection was performed through comprehensive GPS-based field surveys and remote sensing data, validated with ground-based data. The NDWI and MNDWI used to delineate the bankline positions from 1988-2022, with temporal changes estimated using the DSAS model. Findings: The left and right riverbank have been divided into three exclusive zones to find out which zone is severely erosion and accretion prone regions for zone-wise exclusive analysis. In the analysis, it has noticed that the left bank experiencing severe bank erosion and the right bank is experiencing an accretion rate increase in the last few decades. The LRR statistics show that the erosion rate in the left bank (81.17 m/y) is significantly high with an accretion rate of 0 m/y in the observed area. On the other hand, in the right bank, the rate of erosion is 83.59 m/y and the rate of accretion is 68.45 m/y. The left bank’s erosion is notably high. Additionally, the erosion rate is severe in the zone C (118.22 m/y) of left bank line. In this zone, the erosion rate hiked by 59.58 m/y in 34 years. Novelty: This study addresses a significant gap in understanding the riverbank shifting dynamics along with its causes and socio-economic impact of the Brahmaputra River in the Morigaon district. By combining the hydrodynamic modelling and cause-effect analysis, it provides a comprehensive analysis of bankline migration patterns and their causes and consequences. Unlike previous research, which primarily focuses on bankline migration rates and land use changes, this research work uniquely explores the direct impact of riverbank shifting on local communities. The integration of advanced predictive models with detailed primary and secondary data analysis offers novel insights crucial for efficient flood management and policy making practices. Keywords: Normalised Difference Water Index (NDWI), Modified Normalized Difference Index (MNDWI), Digital Shoreline Analysis System (DSAS), EPR and LRR Model, Bankline Shift

Spatio-temporal analysis of shoreline changes and future forecast using remote sensing, GIS and kalman filter model: A case study of Rio de Janeiro, Brazil

Shoreline changes embrace alterations due to the physical features and location of the land-water boundary, which can arise from natural or human causes. Natural processes, like sea level fluctuations, erosion, and sediment deposition, impact coasts. Human activities also significantly influence shoreline changes. The study area's shoreline extends 59.80 km from Reduto de Sao Teodosio to Barra de Guaratiba. The study analyzed shoreline changes along a 60 km stretch of the southern coast of Rio de Janeiro (Reduto de Sao Teodosio to Barra de Guaratiba) from 1986 to 2018. This study examines historical shoreline positions, calculating change rates and predicting 2032 and 2042 positions using satellite imagery spanning 1972–2022. The historical shoreline was reconstructed using earlier satellite data. Digital Shoreline Analysis System (DSAS) tools (EPR, LRR, NSM) in ArcMap were then used to quantify change rates statistically. The EPR model reports a maximum annual accretion rate of 19.33 m and a maximum erosion rate of −4.91 m, while the LRR model indicates a maximum accretion rate of 21.31 m and a maximum erosion rate of −5.04 m. In contrast, the NSM model calculates a notably higher maximum accretion rate of 945.96 m and a much more substantial maximum erosion rate of −240.34 m. These findings informed the mapping of anticipated 2032 and 2042 shorelines, helping identify areas of gain and loss, highlighting the vulnerability of certain regions. It's important to note that human activities, such as alterations in beach topography, can influence these models. Protective measures, like those in Rio de Janeiro, Barra Da Tijuca, and Recreio Dos Bandeirantes, may affect erosion and accretion patterns. Additionally, coastal development activities, including seawall dredging, can disrupt natural processes. Effective management of shoreline changes is essential to safeguard ecosystems and communities, with strategies encompassing beach nourishment, vegetation restoration, and sustainable coastal development. Resilient, adaptive coastal management balances human needs and environmental protection amid ongoing changes.

Effect of a deflector on deposition of particles with different diameters in a rib-roughened channel

A particulate flow in a rib-roughened channel is investigated using LRR model and a Lagrangian method. A deflector is placed in the channel to examine its effect on the deposition rate. The results show that a deflector increases the deposition rate with different diameter sizes by increasing the interaction between particles and lower wall. Particles with 50 μm diameter size have a high response time, hence when their path is changed toward the lower wall, they moved near the rough elements until they leave the channel. This behavior increases deposition velocity with this diameter size by 148.36%. For particles with 10 μm diameter size, it is found that deposition velocity increases by 24.71% due to their low response time. This study shows that while a deflector increases the particle deposition rate, it can cause pressure drop due to flow blockage.

Assessment of human interventions presence and their impact on shoreline changes along Nile delta, Egypt

Coastal erosion is a natural process, that contributed to shaping the Nile Delta shoreline in Egypt over history. The objective of this research is to investigate shoreline changes, accretion, and erosion and to detect particularly vulnerable locations that require immediate attention. Another goal is to assess the efficiency of coastal installations that have been recently implemented along the study area and determine whether they have performed their role to the fullest or need additional modifications. Several Landsat images (TM, ETM+, and OLI) were utilized over 37 years to track the shoreline changes and were analysed using remote sensing (RS) and Geographic Information System (GIS). The digital shoreline analysis system (DSAS) was integrated with the LRR model for assessing historical changes for shorelines from 1985 to 2022 and forecasting future shoreline positions in 2030, 2050, and 2100. Most of the eight zones that make up the Delta region have lately seen the establishment of coastal projects, such as the development of the Abu Qir port's breakwater in 2021. From the results, it was found that the areas around Rosetta promontory, Burullus headland, and Damietta promontory experienced a significant and rapid retreat and with large rates over the study period, with average values of –15.7, –3.25, and –16.8 m/y, respectively. However, both the coast of Alexandria, and Gamasa embayment were subjected to accretion as a prevailing case, with average rates of 2.85, and 4.03 m/y, respectively. Many groins were installed in the east of the Rosetta promontory (zone 3) in 2016 to decrease the erosion process; however, it didn't pay off and could not solve the problem. In contrast, the groins system, which was implemented at the East Kitchener Drain (zone 5) in the same year, lowered erosion rates from 17.6 m/y from 2000 to 2010 to 7 m/y from 2010 to 2022. In 2019, Y-groins built in zone 7, east of Damietta Port, succeeded in slowing rates of erosion. Finally, inlet jetties at El-Gamil (zone 8) were constructed in 2016, resulting in the coastline advancing at 14.7 m/y on average in the period of 2010–2022. The findings of this study confirmed that hard structures are dangerous because they exacerbate the problem of shoreline erosion by disseminating it to the neighbouring beaches and acting as an impediment to the movement of longshore sediments. According to the expected future shoreline patterns, it is necessary for authorities to implement both short-term and long-term protective measures to stop the erosion of several areas of the beach.

FOC winding defect detection based on improved texture features and low-rank representation model.

The defect detection of fiber-optic coils (FOCs) plays an important role in the quality control of the FOC production. In order to overcome the problems of poor performance and low reliability of existing methods, this paper provides a solution for winding defect detection of FOCs based on low-rank representation (LRR) technology. First, we design a feature matrix, which represents the image. Then the LRR model is employed to formulate the defect detection task as a problem of low rank and sparse matrix decomposition. Meanwhile, Laplacian regularization is introduced as a smoothness constraint to expand the distance between defect regions and low-rank background. Experiments are performed on a real dataset to verify the algorithm. The results show that the proposed winding defect detection method of FOCs achieves the highest detection accuracy and lowest false alarm rate compared to other methods, verifying the effectiveness of the proposed method.

Short-run risk, business cycle, and the value premium

We jointly explain the equity and value premium variations in a model with both short-run (SRR) and long-run (LRR) consumption risk. In our empirical analysis, we find that SRR varies with the business cycle, and it has a substantial predictive power for market excess returns and the value premium—both in-sample and out-of-sample. The LRR component also differs significantly from zero, and value stocks have a larger exposure to both LRR and SRR than growth stocks. To explain these patterns in asset returns, we propose an extended LRR model. The model can be solved using log-linear approximations with economically small errors.

Numerical Study of the Turbulent Flow from a Steam Dumping Pressurizer Relief Tank

Due to the complex geometry and turbulent flow characteristics, it is hard to simulate the process of steam dumping of the pressurizer relief tank (PRT). In this study, we develop a compressible fluid solver PRTFOAM to numerically study the turbulent flow dynamics from a PRT. The PRTFOAM is implemented based on the OpenFOAM and designed to be capable of integrating various turbulence models. Two representative Reynolds-averaged Navier–Stokes (RANS) models and a Smagorinsky–Lilly SGS model based on Large Eddy Simulation (LES) are coupled and tested with PRTFOAM. The case of a flow past a circular cylinder (Re = 3900) is tested and analyzed comprehensively as a benchmark case. Then, the turbulent steam dumping process in the full geometry of a PRT is analyzed and compared with ANSYS CFX and literature reports. In addition, we tested the WALE model based on the PRT steam dumping process. The results show that SST k-ω model and Smagorinsky–Lilly SGS model-based LES approach are more appropriate than the LRR model for PRT simulations. Moreover, it shows that the simulation results of Smagorinsky–Lilly SGS model and WALE model are basically consistent under the condition of PRT steam dumping process. Under this condition, the drawbacks of Smagorinsky–Lilly SGS model are not obvious. Furthermore, the comparison with CFX showed that our open source solver could be used to obtain better results in complex engineering cases. The design and testing results would provide guidance for further analysis of thermal-hydraulics in reactors based on open source codes.

Short-run Risk, Business Cycle, and the Value Premium

We jointly explain the variations of the equity and value premium in a model with both short-run (SRR) and long-run (LRR) consumption risk. In our preliminary empirical analysis, we find that SRR varies with the business cycle and it has a substantial predictive power for market excess returns and the value premium|both in-sample and out-of-sample. The LRR component also differs significantly from zero, and value stocks have a larger exposure to both LRR and SRR than growth stocks. To explain these patterns in asset returns, we propose an extended and analytically tractable LRR model.

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

Numerical simulation of flow over ogee crested spillways under high hydraulic head ratio

ABSTRACTOgee spillway is one of the most common types of the spillway. Researchers have attempted to investigate the hydraulics of ogee structure under hydraulic heads near the design head. Herein, an ogee-crested spillway is studied at heads significantly greater than the design head. The efforts are undertaken to study the hydrodynamic field under high head ratio conditions by conducting a numerical simulation using OpenFOAM with five turbulence closures including standard k–ε, realizable k–ε, RNG k–ε, k–ω SST and LRR. The comparisons of flow parameters under different head ratios with the experimental data demonstrated that the LRR model had the best performance, which shows its strength in cases dealing with flow separation or significant streamline curvature. It is found that with increasing hydraulic head, up to seven times that of the design head, the flow separation zone grows linearly. Discharge coefficients are studied for a wide range of head ratios. It is concluded that increasing head ratio up to five leads to an increase in the discharge coefficient due to decreasing pressure on the ogee crest. As head ratio increases to greater values, the discharge coefficient drops suddenly due to some changes in the pressure field.

Iterative Reconstrained Low-Rank Representation via Weighted Nonconvex Regularizer

Benefiting from the joint consideration of geometric structures and low-rank constraint, graph low-rank representation (GLRR) method has led to the state-of-the-art results in many applications. However, it faces the limitations that the structure of errors should be known a prior, the isolated construction of graph Laplacian matrix, and the over shrinkage of the leading rank components. To improve GLRR in these regards, this paper proposes a new LRR model, namely iterative reconstrained LRR via weighted nonconvex regularization, using three distinguished properties on the concerned representation matrix. The first characterizes various distributions of the errors into an adaptively learned weight factor for more flexibility of noise suppression. The second generates an accurate graph matrix from weighted observations for less afflicted by noisy features. The third employs a parameterized rational function to reveal the importance of different rank components for better approximation to the intrinsic subspace structure. Following a deep exploration of automatic thresholding, parallel update, and partial SVD operation, we derive a computationally efficient low-rank representation algorithm using an iterative reconstrained framework and accelerated proximal gradient method. Comprehensive experiments are conducted on synthetic data, image clustering, and background subtraction to achieve several quantitative benchmarks as clustering accuracy, normalized mutual information, and execution time. Results demonstrate the robustness and efficiency of IRWNR compared with other state-of-the-art models.

Weighted Low-Rank Representation-Based Dimension Reduction for Hyperspectral Image Classification

A predimension-reduction algorithm that couples weighted low-rank representation (WLRR) with a skinny intrinsic mode functions (IMFs) dictionary is proposed for hyperspectral image (HSI) classification. It seeks a low-rank subspace to solve the performance degradation issue encountered by linear discriminant analysis in a small-sample-size situation. It can also improve the scatter matrix estimation when using a large training set. Unlike those commonly used methods, e.g., the principal component analysis-based ones, WLRR focuses on preserving more structure information. Based on the traditional LRR model, WLRR introduces a local weighted regularization to characterize the correlation between samples such that HSI-specific local structure can be better preserved as well as its global structure. Indeed, more structure information gives more additional discriminant ability. Furthermore, a new discriminant IMFs dictionary is designed to enhance interclass difference via empirical mode decomposition. The proposed method is investigated on several HSI data sets. All experimental results prove it a competitive and promising predimension-reduction means when compared to other traditional techniques.

On the compressibility effects in mixing layers

Previous studies of compressible flows carried out in the past few years have shown that the pressure-strain is the main indicator of the structural compressibility effects. Undoubtedly, this terms plays a key role toward strongly changing magnitude of the turbulent Reynolds stress anisotropy. On the other hand, the incompressible models of the pressure-strain correlation have not correctly predicted compressible turbulence at high speed shear flow. Consequently, a correction of these models is needed for precise prediction of compressibility effects. In the present work, a compressibility correction of the widely used incompressible Launder Reece and Rodi model making their standard coefficients dependent on the turbulent and convective Mach numbers is proposed. The ability of the model to predict the developed mixing layers in different cases from experiments of Goebel and Dutton is examined. The predicted results with the proposed model are compared with DNS and experimental data and those obtained by the compressible model of Adumitroiae et al. and the original LRR model. The results show that the essential compressibility effects on mixing layers are well captured by the proposed model.

Locality-preserving low-rank representation for graph construction from nonlinear manifolds

Building a good graph to represent data structure is important in many computer vision and machine learning tasks such as recognition and clustering. This paper proposes a novel method to learn an undirected graph from a mixture of nonlinear manifolds via Locality-Preserving Low-Rank Representation (L2R2), which extents the original LRR model from linear subspaces to nonlinear manifolds. By enforcing a locality-preserving sparsity constraint to the LRR model, L2R2 guarantees its linear representation to be nonzero only in a local neighborhood of the data point, and thus preserves the intrinsic geometric structure of the manifolds. Its numerical solution results in a constrained convex optimization problem with linear constraints. We further apply a linearized alternating direction method to solve the problem. We have conducted extensive experiments to benchmark its performance against six state-of-the-art algorithms. Using nonlinear manifold clustering and semi-supervised classification on images as examples, the proposed method significantly outperforms the existing methods, and is also robust to moderate data noise and outliers.

A compressibility correction of the pressure strain correlation model in turbulent flow

This paper is devoted to the second-order closure for compressible turbulent flows with special attention paid to modeling the pressure–strain correlation appearing in the Reynolds stress equation. This term appears as the main one responsible for the changes of the turbulence structures that arise from structural compressibility effects. From the analysis and DNS results of Simone et al. and Sarkar, the compressibility effects on the homogeneous turbulence shear flow are parameterized by the gradient Mach number. Several experiment and DNS results suggest that the convective Mach number is appropriate to study the compressibility effects on the mixing layers. The extension of the LRR model recently proposed by Marzougui, Khlifi and Lili for the pressure–strain correlation gives results that are in disagreement with the DNS results of Sarkar for high-speed shear flows. This extension is revised to derive a turbulence model for the pressure–strain correlation in which the compressibility is included in the turbulent Mach number, the gradient Mach number and then the convective Mach number. The behavior of the proposed model is compared to the compressible model of Adumitroiae et al. for the pressure–strain correlation in two turbulent compressible flows: homogeneous shear flow and mixing layers. In compressible homogeneous shear flows, the predicted results are compared with the DNS data of Simone et al. and those of Sarkar. For low compressibility, the two compressible models are similar, but they become substantially different at high compressibilities. The proposed model shows good agreement with all cases of DNS results. Those of Adumitroiae et al. do not reflect any effect of a change in the initial value of the gradient Mach number on the Reynolds stress anisotropy. The models are used to simulate compressible mixing layers. Comparison of our predictions with those of Adumitroiae et al. and with the experimental results of Goebel et al. shows good qualitative agreement.

A Numerical Investigation on the Development of an Embedded Streamwise Vortex in a Turbulent Boundary Layer With Spanwise Pressure Gradient

It is the aim of this article to investigate numerically the effects of spanwise pressure gradient on an embedded streamwise vortex in a turbulent boundary layer. The governing equations were discretized by the finite volume method and SIMPLE algorithm was used to couple between pressure and velocity. The LRR model for Reynolds stresses was utilized to predict the anisotropy of turbulence effectively. The validation was done for two cases: one is the development of a streamwise vortex embedded in a pressure-driven, three-dimensional turbulent boundary layer. The other involves streamwise vortex pairs embedded in a turbulent boundary layer without the spanwise pressure gradient. In the case of the former, the predicted results were compared with Shizawa and Eaton’s experimental data. In the latter case, the calculated results were compared against the experimental data of Pauley and Eaton. We performed numerical simulations for three cases with different values of spanwise pressure gradient. As a result, the primary streamwise vortex with spanwise pressure gradients decays more rapidly than the case with no pressure gradients, as the spanwise pressure gradient increases. This indicates that the spanwise pressure gradient may play an important role on mean and turbulent structures. In particular, it can be seen that the increase of pressure gradient enhances a level of turbulent normal stresses.

A PERFORMANCE COMPARISON OF THE STANDARD k-ϵ MODEL AND A DIFFERENTIAL REYNOLDS STRESS MODEL FOR A BACKWARD-FACING STEP

A differential Reynolds stress model (RSM) based on Launder, Reece, and Rodi (LRR) is formulated with appropriate wall Junctions and applied to predict the backward-facing step problem of Driver and Seegmiller. Numerical prediction obtained with the LRR, with and without “wall reflection” terms in the pressure strain model, are compared with the results of standard k-e model of Launder and Spalding for the step problem. The results demonstrate that both LRR models, i.e., with and without wall reflection terms, are capable of capturing the secondary bubble near the step, as observed in the experiment, whereas the standard k-ϵ model fails to predict the secondary bubble. In addition, the mean velocity profiles obtained with the LRR models agree better with the experimental data than those by the k-ϵ model, particularly inside the recirculating flow region. It also emerges from the present study that, with proper wall functions, LRR model is capable of predicting recirculating flows at least as well as the original LRR model does without the “wall reflection” terms.

Application of Reynolds Stress Modeling to Engine Flow Calculations

To improve the prediction of turbulence inside internal combustion engines, a Reynolds stress turbulence model is implemented in the Kiva-II code. After a rapid description of the Launder-Reece-Rodi model (noted LRR), two validation test cases (the plane channel flow and the flow over a backward facing step) are presented. The advantages of a second order closure and the shortcomings of the LRR model are then analyzed. Finally, a simulation of an intake and compression stroke using both the standard k – ε model and the LRR model is described. As a precise knowledge of the velocity and turbulent fields near TDC is necessary for the prediction of the mixing and the combustion processes, we have analyzed the influence of the turbulence model on the flow field. Results are compared with experimental data and show a strong influence of the turbulence model even on the mean flow, especially at the end of the compression stroke (TDC).

Computation of backward‐facing step flows by a second‐order Reynolds stress closure model

AbstractThis paper scrutinizes the predictive ability of the differential stress equation model in complex shear flows. Two backward‐facing step flows with different expansion ratios are solved by the LRR turbulence model with an anisotropic dissipation model and the near‐wall regions of the separated side resolved by a near‐wall model. The computer code developed for solving the transport equations is based on the finite‐volume‐finite‐difference method. In the numerical solution of the time‐averaged momenum equations the Reynolds stresses are treated partially as a diffusion term and partially as a source term to avoid numerical instability. Computational results are compared with experimental data. It is found that the near‐wall region of the separated side resolved by the near‐wall model, the LRR model with a simple modification of an anisotropic dissipation model can predict backward step flows well.

Influence of the turbulence model in calculations of flow over obstacles with second-moment closures

This paper investigates the role of turbulence models in numerical calculations of flow over obstacles with second-moment closure models. Two models for the pressure-strain correlations are examined in the study. Computations of the main characteristics of the mean flow and the turbulent fields are compared against experimental data, and results obtained with the standard k-ϵ model. All models give reasonable agreement with the data. In the limited region in which comparisons were made, the k-ϵ model gives the best agreement with mean flow data, and the LRR model gives better agreement with Reynolds stress data.